Forecasting Geomagnetic Storms for Earth Using LSTM with Solar Wind and IMF Features

When the solar wind interacts with Earth’s field, it can cause geomagnetic storms, which pose serious risks to vital infrastructure such as satellite communication, GPS systems, and electric power transmission. We presented a novel method for forecasting the Disturbance Storm-Time Index (Dst) using a long-short-term memory (LSTM) neural network in order to meet the urgent requirement for precise predictions. Our model employs sensing data from NASA’s ACE and NOAA’s DSCOVR satellites to analyze the complex relationship between interplanetary magnetic field & plasma from solar wind & sunspot activity. These factors are crucial to understanding geomagnetic disturbances. The LSTM model accumulates complex patterns and temporal relationships within the space weather data by including real-time data assimilation, resulting in enhanced weather prediction capabilities. Evaluation metrics like the root mean square error (RMSE) and coefficient of determination ($R^{2}$) are used to assess the model’s performance and reliability. The outcomes indicate that the LSTM model is highly effective in accurately predicting geomagnetic storms. The predicted values offer vital information for operators of satellites, power grids and magnetic navigation systems, enabling them to implement preemptive actions to mitigate the effects of any disruptions caused by geomagnetic storms. The research we accomplished helps to improve the approaches employed for forecasting space weather. This provides decision-makers with a useful and timely tool to strengthen vital systems and prepare for geomagnetic disturbances.